Bayesian feature selection and model detection for student´s t-mixture distributions

In this paper, we propose a novel method for feature selection and model detection using Student´s t-distributions based on the variational Bayesian (VB) approach. First, our method is based on the Student´s t-mixture model which has heavier tails than the Gaussian distribution and is therefore less sensitive to small numbers of data points and consequent precision-estimates of the components number. Second, the number of components, the local feature saliency and the parameters of the mixture model are simultaneously estimated by Bayesian variational learning.